Identifying causal mechanisms is a fundamental goal of social
science. Researchers seek to study not only whether one variable
affects another but also how such a causal relationship arises. Yet,
commonly used statistical methods for identifying causal mechanisms
rely upon untestable assumptions and are often inappropriate even
under those assumptions. Randomizing treatment and intermediate
variables is also insufficient. Despite these difficulties, study
of causal mechanisms is too important to abandon. We make three
contributions to improve research on causal mechanisms. First, we
present a minimum set of assumptions required under standard designs
of experimental and observational studies and develop a general
algorithm for estimating causal mediation effects. Second, we
provide a method to assess sensitivity of conclusions to potential
violations of a key assumption. Third, we offer alternative research
designs for identifying causal mechanisms under weaker assumptions.
The proposed approach is illustrated using media framing experiments
and incumbency advantage studies.